The present embodiments relate to detecting markers in medical data. In particular, marker detection for medical imaging, such as imaging a cardiac stent, is provided.
Coronary heart disease is the most common cause of death for men and women. To treat narrowing of the artery lumen due to accumulation of atheromatous plaques, a stent is implanted to expand and support the narrowing vessel. A stent is a fine mesh. A guide-wire is first introduced inside the artery. During implantation, the stent is supported by a balloon. The angioplasty balloon equipped with a stent is slid along the guide wire.
Stent deployment is monitored by X-ray fluoroscopy. Proper visualization of the stent, including the stent's location, surrounding tissue and deployed geometry, is important to ensure the quality of stent expansion. Under-deployment of the stent is associated with restenosis and thrombosis. However, some stents have less metal, so are less radiopaque. Drug eluting stents may also be less radiopaque than bare metal stents. Lower X-ray doses may be desired, but result in less image quality. As a result, stent visibility in X-ray images is challenging.
One technique for stent enhancement in X-ray fluoroscopy image sequences is based on motion compensated temporal integration of the stent images. To compensate for heart and/or breathing motion, image frames in an X-ray sequence are registered to align the stent. In order to assess the location of the stent, the angioplasty balloon is equipped with two heavily radiopaque markers. The image frames are aligned based on the highly contrasted balloon markers. Temporal averaging of all aligned images may allow preserving stent contrast in the image while blurring the background and suppressing noise, leading to improved stent visibility. While the moving background in the registered image sequence is blurred, the stent is preserved.
The markers may be detected in the images using match filters. A template of expected intensity response of the markers is correlated with the image. The highest correlation indicates the location of the markers. Markers may alternatively be detected using blob detection with automatic scale selection. Uniform intensity distribution within a shape is identified. This “blob” information is searched for in data at different scales. However, these marker detection techniques may have limited capability to cope with large variations and cluttered background presented in real applications. For instance, some patients have had previous interventions, such as open-heart surgery or stent placement. The placed sternal wires, stitches, stents, and other devices in such patients introduce locally similar structures to the balloon markers. Such similar structures may result in detection of a significant number of false markers. Large variations of marker appearance across time may also make it difficult for conventional detection algorithms to consistently differentiate balloon markers, especially when the target markers are overlaid with other structures in the image. Conventional balloon marker detection may depend upon temporal coherence to compensate detection errors among individual image frames and may require user interactions to achieve desired performance.